Underpinning Solvency II compliance with data governance

Like other European insurers, AGCS has been tasked with demonstrating compliance with the Solvency II Directive. The Directive stipulates that data used to calculate capital adequacy must be accurate, appropriate, complete and timely. There is also an onus on insurers to define a system of measurement for the quality of business data used to feed capital adequacy models, with structures that ensure appropriate checks and balances are performed – including board-level oversight.

At AGCS this meant addressing data quality for 32 separate data flows which relate to Solvency II and which flow into the Risk Capital Model. In addition, the company recognized the chance for increased competitive advantage and improved efficiency.

High-quality data and the ability to report on data quality over time underpins compliance with Solvency II, but we also believe it gives us greater operational efficiency and competitiveness.

Rolf Neuerburg
Data Governance Manager

AGCS needed to create an approach to data governance that included a technology solution capable of designing and executing business rules to improve data quality as well as providing a means to represent data quality metrics over time.

In addition to the technology, the company had to create a corporate structure able to monitor and respond to changing data quality metrics, assign budgets for improvement projects and provide technical expertise throughout the organization.

The solution

With the goal of understanding, improving and measuring its data, AGCS turned to a SAS data management platform as its central design and execution environment for data quality business rules. In addition, a SAS data quality dashboard was integrated with the platform to deliver visibility into data quality reports to a wide cross section of management.

AGCS also created a well-defined data governance structure composed of a steering committee which includes the COO, CIO and other senior managers, and a data governance team, which includes data management experts offering consultancy and guidance to the rest of the organization.

The structure is accompanied by a clear data improvement process. When the data governance team is alerted to a data area which displays poor metrics, the root causes are investigated and the project is passed to the steering committee for review and budget allocation.

When signoff is achieved, the data governance team works with "data consumers" to define business rules within the data management platform to improve quality. The team also coordinates with "data producers" to help rectify business processes that result in poor quality data. Business rules are then monitored and reported on over time.

The results

From a standing start AGCS has moved quickly, implementing SAS technology and witnessing a 15-percen improvement in data quality metrics across measured datasets.

The organization has worked closely with data consumers to define an organizationwide data policy and has already established a clear structure and process for data governance.

AGCS now has the ability to measure data quality scores across all areas of its risk data such as credit and market risk, insurance risk and business risk data.

SAS® Data Governance allows organizations to identify weaknesses in the data which can be clearly represented to senior management in order to achieve signoff for the resources required to tackle them.

Rolf Neuerburg, Data Governance Manager at AGCS, commented: "We now have a process and organizational structure, which means we can ask 'how reliable is the data upon which we base our decisions?' The answers we receive are granular, which allows us demonstrate the impact data improvement projects will have, making it easier to gain sign off from senior management."

Culturally, the company has successfully put data governance at the heart of its organization by involving all areas of the business. This has enabled AGCS to create an environment where operational data is both governed and can now be used for commercial advantage on top of regulatory compliance.

Neuerberg continued: "High-quality data and the ability to report on data quality over time underpins compliance with Solvency II, but we also believe it gives us greater operational efficiency and competitiveness. That's why our data governance program extends to all our data, not just that deemed applicable to Solvency II."

Challenge

Solution

Benefits

Improved overall data quality metrics by 15%.

Established a global Six Sigma quality-improvement process with reported regularly metrics through an integrated BI dashboard.

Business rules can now be applied to each of the 32 Solvency II data flows before the data enters the company’s risk capital model, providing a high degree of accuracy, appropriateness and completeness.

The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.